Computational Identification and Characterization of Potential T-Cell Epitope for the Utility of Vaccine Design Against Enterotoxigenic Escherichia coli
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Diarrhea is considered to be a leading cause of death in children less than 5 years of age and the major cause of diarrheal diseases in infants and young children as well as in individuals traveling to endemic areas are caused by Enterotoxigenic Escherichia coli (ETEC) bacteria. Despite the severe outbreaks of diarrheal infections, increasing resistance to antibiotics and slow experimental analysis, there is a lack of an effective vaccine. T-cell epitope mapping is a desirable method in predicting the antigenic proteins and epitope-based vaccines have remarkable privilege over the conventional ones since they are specific, able to avoid undesirable immune responses, generate long lasting immunity, and are reasonably cheaper. In the present study, prediction and modeling of the T cell epitopes of enterotoxigenic Escherichia coli antigenic proteins along with the binding simulation studies of the highest binding scorers with their corresponding MHC class II alleles were performed. Here, an immunoinformatic tool ProPred was used to predict the promiscuous MHC class II restricted T-cell epitopes of the bacterial antigenic proteins. Further, different computational tools were used to analyze the physiochemical properties of the most immunogenic protein that can be manipulated as a potential candidate in the development of vaccine. Docking of the predicted epitopes was tested to detect the binding efficiency with MHC II alleles. After rigorous investigations, the 3 epitopes- YKFVPWFNL, LNIDITGCA and MKASNGLNI were found to be the most potential T cell epitopes. Therefore, peptides predicted through various computational approaches may have the potential to trigger an effective T cell-mediated immune response and can be suggested for experimental analysis to validate their antigenic properties as a suitable vaccine candidate.
KeywordsEpitope Docking Immunoinformatic MHC Peptides
The authors gratefully acknowledge the necessary computational facilities and constant supervision provided by Department of Biotechnology, Faculty of Engineering and Technology, Rama University Uttar Pradesh Kanpur, India for their generous support during the research work.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
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